Direct-acting antiviral treatment uptake and sustained virological response outcomes are not affected by alcohol use: A CANUHC analysis
Bibliographic record
Abstract
BACKGROUND: Alcohol use and hepatitis C virus (HCV) are two leading causes of liver disease. Alcohol use is prevalent among the HCV-infected population and accelerates the progression of HCV-related liver disease. Despite barriers to care faced by HCV-infected patients who use alcohol, few studies have analyzed uptake of direct-acting antiviral (DAA) treatment. OBJECTIVE: We compared rates of treatment uptake and sustained virological response (SVR) between patients with and without alcohol use. METHODS: Prospective data were obtained from the Canadian Network Undertaking against Hepatitis C (CANUHC) cohort. Consenting patients assessed for DAA treatment between January 2016 and December 2019 were included. Demographic and clinical characteristics were compared between patients with and without alcohol use by means of t-tests, χ2 tests, and Fisher’s Exact Tests. Univariate and multivariate analyses were used to determine predictors of SVR and treatment initiation. RESULTS: Current alcohol use was reported for 217 of 725 (30%) patients. The proportion of patients initiating DAA treatment did not vary by alcohol use status (82% versus 83%; p = 0.99). SVR rate was similar between patients with alcohol use and patients without alcohol use (92% versus 94%; p = 0.45). Univariate and multivariate analysis found no association between alcohol use and SVR or treatment initiation. CONCLUSION: Patients engaged in HCV treatment have highly favourable treatment uptake and outcomes regardless of alcohol use. Public health interventions should be directed toward facilitating access to care for all patients irrespective of alcohol use. Research into high-level alcohol use and DAA outcomes is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".